Prompt engineers for production AI features — eval pipelines, RAG, agent workflows, output reliability.
Prompt engineers for production AI features — eval pipelines, RAG, agent workflows, output reliability. Every profile in this catalog is hand-vetted: real production work with Prompt Engineering, public portfolio or live cases, and recent client reviews. No marketplace rotation — only specialists with shipped artefacts.
The catalog shortens the path from "I need a Prompt Engineering expert" to a signed contract with a specific person. Each card carries the stack, rate format (hourly / monthly / project fix), engagement availability and a direct contact channel.
Tasks the catalog handles regularly: shipping production features with Chatgpt, Claude, Gemini, Perplexity, eval pipelines and quality gates, integration with existing ops (CRM, support, content publishing), team training and process documentation.
Expert work covers the whole lifecycle: scope definition, prototype, production deploy, and the unglamorous follow-up — fixes, optimisations, handover to internal teams. Hire a specialist for one slice or wire them in as a fractional lead for a quarter.
Most Prompt Engineering experts work in three formats:
Open the cards, compare rates, portfolios and recent reviews. Reach out directly via the contact channel listed on the profile. First consultation is free with most experts — useful for evaluating fit and tightening scope before signing.
For non-standard tasks use the "Post a project" form: brief goes to the closed top-expert pool and matching specialists reply with proposals. No platform commission — payouts go straight to the expert.
Pick an expert based on rate band and stack overlap with your task. If unsure, post a brief — closing the loop with 2-3 proposals usually surfaces the right fit faster than scrolling. Browse the 2026 catalog above to get started.
Pricing scales with complexity: a one-off audit or tuning a single prompt costs far less than building a full RAG system with an eval pipeline and agents. Data volume, the number of scenarios, reliability requirements and the format — hourly, fixed-project or monthly retainer — all move the price.
Look for real production work, not just «nice prompts»: experience with your target models (Claude, GPT, Gemini), the ability to build evals and RAG, and familiarity with your domain. A small paid test task on your own data is the best filter.
They can't turn a model into a source of absolute truth — even perfect prompts leave some hallucination risk, so critical tasks still need checks, RAG and guardrails. They also don't replace full backend development; they usually work alongside your own engineers.
Prompt audits and optimization can take a few days; a RAG system with evals is typically 2–4 weeks or more. You receive version-controlled prompts, output schemas, an eval set with metrics, and documentation so your team can maintain it.